Low-illumination image enhancement method based on improved Retinex and logarithm image processing
An image processing and image enhancement technology, applied in image enhancement, image data processing, image analysis, etc., can solve problems such as scenes that cannot meet complex lighting, color distortion, etc., to solve the effect of halo effect and over-enhancement
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Embodiment 1
[0044] Such as figure 1 Shown, based on the low illumination image enhancement method of improved Retinex and logarithmic image processing, it is characterized in that, comprises the following steps:
[0045] S1: Calculate the bright channel value of the sample image, and use this value as the illumination component of the sample image;
[0046] S2: Use the background intensity under the existing logarithmic image processing model to perform adaptive local adjustment on the illumination component;
[0047] S3: Combining with the Sobel edge detection method, filter and refine the locally adjusted illumination components;
[0048] S4: According to the refined illumination component, the enhanced image is obtained based on the Retinex theory.
[0049] In the specific implementation process, this method combines the Retinex theory under the existing logarithmic image processing model to strengthen the sample image, effectively solving the halo effect and over-enhancement problem...
Embodiment 2
[0051] More specifically, on the basis of Example 1, the image collected by any image acquisition device is used as a sample image, and based on the bright channel prior theory, the sample image is separated into color channels, and the three color channel images are compared pixel by pixel. , extract the maximum pixel point and save it as the maximum value image; then perform maximum value filtering on the obtained maximum value image, so as to obtain the illumination component of the sample image, the result is as follows figure 2 shown.
[0052] In the specific implementation process, based on the logarithmic image processing model, the illumination component of the sample image is converted into an expression under the logarithmic image processing model, that is, the gray value of the sample image is expressed as:
[0053] f(x,y)=M-I(x,y);
[0054] In the above formula, I(x, y) represents the pixel value of the sample image, f(x, y) is the gray value of the image under t...
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